Journal ArticleDOI
Semantic loop closure detection based on graph matching in multi-objects scenes
TLDR
A strategy that models the visual scene as semantic sub-graph by only preserving the semantic and geometric information from object detection is presented, which indicates that this semantic graph-based representation without extracting visual features is feasible for loop-closure detection at potential and competitive precision.About:
This article is published in Journal of Visual Communication and Image Representation.The article was published on 2021-04-01. It has received 28 citations till now. The article focuses on the topics: Object detection & Graph (abstract data type).read more
Citations
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Journal Article
SeqSLAM : visual route-based navigation for sunny summer days and stormy winter nights
Michael Milford,Gordon Wyeth +1 more
TL;DR: A new approach to visual navigation under changing conditions dubbed SeqSLAM, which removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images.
Journal ArticleDOI
An Overview on Visual SLAM: From Tradition to Semantic
Weifeng Chen,Guang Peng Shang,A. Xiaolan Ji,Chengjun Zhou,Xiyang Wang,Chonghui Xu,Zhenxiong Li,Kai Hu +7 more
TL;DR: This paper introduces the development of VSLAM technology from two aspects: traditional V SLAM and semantic VSLam combined with deep learning, and focuses on the developmentof semantic V SLam based on deep learning.
Proceedings ArticleDOI
Hydra: A Real-time Spatial Perception System for 3D Scene Graph Construction and Optimization
TL;DR: The proposed Spatial Perception System is implemented into a highly parallelized architecture, named Hydra 1, that combines fast early and mid-level perception processes with slower high- level perception processes and global optimization of the scene graph.
Journal Article
Hydra: A Real-time Spatial Perception Engine for 3D Scene Graph Construction and Optimization
TL;DR: This paper describes the first real-time Spatial Perception engINe (SPIN), a suite of algorithms to build a 3D scene graph from sensor data in real- time that is implemented into a highly parallelized architecture that combines fast early and mid level perception processes with slower highlevel perception processes.
Journal ArticleDOI
Kalman Filtering and Bipartite Matching Based Super-Chained Tracker Model for Online Multi Object Tracking in Video Sequences
Shahzad Ahmad Qureshi,Lal Hussain,Quratul-Ain Chaudhary,Syed Rahat Abbas,Raja Junaid Khan,Amjad Ali,Ala Al-Fuqaha +6 more
TL;DR: A Super Chained Tracker (SCT) model is proposed, which is convenient and online and provides better results when compared with existing MOT methods, and comprises subtasks, object detection, feature manipulation, and using representation learning into one end-to-end solution.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Proceedings ArticleDOI
Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Proceedings ArticleDOI
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
TL;DR: RCNN as discussed by the authors combines CNNs with bottom-up region proposals to localize and segment objects, and when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost.
Proceedings ArticleDOI
Fast R-CNN
TL;DR: Fast R-CNN as discussed by the authors proposes a Fast Region-based Convolutional Network method for object detection, which employs several innovations to improve training and testing speed while also increasing detection accuracy and achieves a higher mAP on PASCAL VOC 2012.